Datasets:
HenryAI
/
KerasCodeExamples.txt

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label = tf.strings.reduce_join(num_to_char(indices))
label = label.numpy().decode(\"utf-8\")
ax[i // 4, i % 4].imshow(img, cmap=\"gray\")
ax[i // 4, i % 4].set_title(label)
ax[i // 4, i % 4].axis(\"off\")
plt.show()
png
You will notice that the content of original image is kept as faithful as possible and has been padded accordingly.
Model
Our model will use the CTC loss as an endpoint layer. For a detailed understanding of the CTC loss, refer to this post.
class CTCLayer(keras.layers.Layer):
def __init__(self, name=None):
super().__init__(name=name)
self.loss_fn = keras.backend.ctc_batch_cost
def call(self, y_true, y_pred):
batch_len = tf.cast(tf.shape(y_true)[0], dtype=\"int64\")
input_length = tf.cast(tf.shape(y_pred)[1], dtype=\"int64\")
label_length = tf.cast(tf.shape(y_true)[1], dtype=\"int64\")
input_length = input_length * tf.ones(shape=(batch_len, 1), dtype=\"int64\")
label_length = label_length * tf.ones(shape=(batch_len, 1), dtype=\"int64\")
loss = self.loss_fn(y_true, y_pred, input_length, label_length)
self.add_loss(loss)
# At test time, just return the computed predictions.
return y_pred
def build_model():
# Inputs to the model
input_img = keras.Input(shape=(image_width, image_height, 1), name=\"image\")
labels = keras.layers.Input(name=\"label\", shape=(None,))
# First conv block.
x = keras.layers.Conv2D(
32,
(3, 3),
activation=\"relu\",
kernel_initializer=\"he_normal\",
padding=\"same\",
name=\"Conv1\",
)(input_img)
x = keras.layers.MaxPooling2D((2, 2), name=\"pool1\")(x)
# Second conv block.
x = keras.layers.Conv2D(
64,
(3, 3),
activation=\"relu\",
kernel_initializer=\"he_normal\",
padding=\"same\",
name=\"Conv2\",
)(x)
x = keras.layers.MaxPooling2D((2, 2), name=\"pool2\")(x)
# We have used two max pool with pool size and strides 2.
# Hence, downsampled feature maps are 4x smaller. The number of
# filters in the last layer is 64. Reshape accordingly before
# passing the output to the RNN part of the model.
new_shape = ((image_width // 4), (image_height // 4) * 64)
x = keras.layers.Reshape(target_shape=new_shape, name=\"reshape\")(x)
x = keras.layers.Dense(64, activation=\"relu\", name=\"dense1\")(x)
x = keras.layers.Dropout(0.2)(x)
# RNNs.
x = keras.layers.Bidirectional(
keras.layers.LSTM(128, return_sequences=True, dropout=0.25)
)(x)
x = keras.layers.Bidirectional(
keras.layers.LSTM(64, return_sequences=True, dropout=0.25)
)(x)
# +2 is to account for the two special tokens introduced by the CTC loss.
# The recommendation comes here: https://git.io/J0eXP.
x = keras.layers.Dense(
len(char_to_num.get_vocabulary()) + 2, activation=\"softmax\", name=\"dense2\"
)(x)
# Add CTC layer for calculating CTC loss at each step.
output = CTCLayer(name=\"ctc_loss\")(labels, x)
# Define the model.
model = keras.models.Model(
inputs=[input_img, labels], outputs=output, name=\"handwriting_recognizer\"
)
# Optimizer.
opt = keras.optimizers.Adam()
# Compile the model and return.
model.compile(optimizer=opt)
return model
# Get the model.